My latest research paper[1] deals with the estimation of the hemodynamic response function (HRF) from fMRI data.

This is an important topic since the knowledge of a hemodynamic response function is what makes it possible to extract the brain activation maps that are used in most of the impressive applications of machine learning to fMRI, such as (but not limited to) the reconstruction of visual images from brain activity [2] [3] or the decoding of numbers [4].

Besides the more traditional paper that describes the method, I've put online the code I used for the experiments. The code at this stage is far from perfect but it should help in reproducing the results or improving the method. I've also put online an ipython notebook with the analysis of a small piece of data. I'm obviously glad to receive feedback/bug reports/patches for this code.

Other articles

One of the lesser known features of the memory_profiler package is its ability to plot memory consumption as a function of time. This was implemented by my friend Philippe Gervais, previously a colleague at INRIA and now working for Google.

Following a challenge proposed by Gael to my
group I compared several implementations of
Logistic Regression. The task was to implement a Logistic Regression model
using standard optimization tools from scipy.optimize and compare
them against state of the art implementations such as
LIBLINEAR.

My latest contribution for scikit-learn is
an implementation of the isotonic regression model that I coded with
Nelle Varoquaux and
Alexandre Gramfort. This model
finds the best least squares fit to a set of points, given the
constraint that the fit must be a non-decreasing
function. The example
on the ...

Besides performing a line-by-line analysis of memory consumption,
memory_profiler
exposes some functions that allow to retrieve the memory consumption
of a function in real-time, allowing e.g. to visualize the memory
consumption of a given function over time.

The function to be used is memory_usage. The first argument
specifies what ...